DeepSeek V3 vs Llama 3.3 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek V3 by DeepSeek and Llama 3.3 70B by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 3.3 70B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.3 70B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Llama 3.3 70B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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DeepSeek V3
Open-Weights-ModellDeepSeek V3 von DeepSeek ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $0.27/1M Input- und $1.1/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.27 / 1M
$1.10 / 1M
128K tokens
49.2%
Llama 3.3 70B
Open-Weights-ModellLlama 3.3 70B von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $0.0/1M Input- und $0.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
Wichtigste Unterschiede auf einen Blick
- Llama 3.3 70B is 10.0x cheaper for API usage (input + output tokens combined).
- Llama 3.3 70B is faster at 280 tok/s vs 60 tok/s.
- DeepSeek V3 leads on SWE-bench coding (49.2% vs 39.5%).
| Feature | DeepSeek V3 | Llama 3.3 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 38 |
Provider | DeepSeek | Meta |
Input Price Cost per million input tokens | $0.27 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.10 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $0.07 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 60 tok/s | 280 tok/s |
SWE-bench Verified Real-world software engineering | 49.2% | 39.5% |
GPQA Diamond Graduate-level reasoning | 59.1% | 48.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $0.10 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek V3 · Strengths & Key Advantages
- Fortschrittliche 671B Mixture-of-Experts (37B active)-Architektur von DeepSeek mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.27/1M Input, $1.1/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Llama 3.3 70B · Strengths & Key Advantages
- Fortschrittliche Dense Autoregressive Transformer-Architektur von Meta mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.0/1M Input, $0.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek V3 | Llama 3.3 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.14 | $0.21 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek V3 | Llama 3.3 70B |
|---|---|---|
| Official API Model ID (SDK) | deepseek-chat | meta-llama/Llama-3.3-70B |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-12 | 2024-12 |
| Knowledge Cutoff | July 2024 | December 2023 |
| Architecture | 671B Mixture-of-Experts (37B active) | Dense Autoregressive Transformer |
| Parameters | 671B | 70B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.27/1M in) across full 128K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.07/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
DeepSeek V3 vs Llama 3.3 70B: Schnelle Antworten (FAQ)
Which is more intelligent, DeepSeek V3 or Llama 3.3 70B?
DeepSeek V3 is more intelligent. It scores 44 compared with 38 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 70B is faster, generating 280 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $0.21 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.68s.
Fazit
Verdict & Model Selection Recommendation: Choose DeepSeek V3 if your priority is High-volume production APIs, budget-conscious full-stack development, and bulk document transformation. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.3 70B if your workflow prioritizes Private on-premises deployments, high-throughput text summarization, and fine-tuned domain tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does DeepSeek V3 compare to Llama 3.3 70B in API token pricing and cost per token?+
DeepSeek V3 costs $0.27 per 1M input tokens and $1.10 per 1M output tokens, whereas Llama 3.3 70B costs $0.00 input and $0.00 output. Overall, Llama 3.3 70B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: DeepSeek V3 or Llama 3.3 70B?+
Both DeepSeek V3 and Llama 3.3 70B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: DeepSeek V3 or Llama 3.3 70B?+
DeepSeek V3 scores 49.2% on SWE-bench Verified while Llama 3.3 70B scores 39.5%. For autonomous software engineering and multi-file debugging, DeepSeek V3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek V3 and Llama 3.3 70B?+
DeepSeek V3 is optimized for low-latency direct generation. In contrast, Llama 3.3 70B focuses on standard fast inference.
How does inference speed (tokens per second) compare between DeepSeek V3 and Llama 3.3 70B?+
DeepSeek V3 generates approximately 60 tokens per second, while Llama 3.3 70B delivers 280 tokens per second. In latency-sensitive production environments, Llama 3.3 70B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek V3 and Llama 3.3 70B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.3 70B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek V3. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs